The wild species of the genus Oryza contain a largely untapped reservoir of agronomically important genes for rice improvement. Here we report the 261-Mb de novo assembled genome sequence of Oryza brachyantha. Low activity of long-terminal repeat retrotransposons and massive internal deletions of ancient long-terminal repeat elements lead to the compact genome of Oryza brachyantha. We model 32,038 protein-coding genes in the Oryza brachyantha genome, of which only 70% are located in collinear positions in comparison with the rice genome. Analysing breakpoints of non-collinear genes suggests that double-strand break repair through non-homologous end joining has an important role in gene movement and erosion of collinearity in the Oryza genomes. Transition of euchromatin to heterochromatin in the rice genome is accompanied by segmental and tandem duplications, further expanded by transposable element insertions. The high-quality reference genome sequence of Oryza brachyantha provides an important resource for functional and evolutionary studies in the genus Oryza.

Plant volatile organic compounds, which are generated in a tissue-specific manner, play important ecological roles in the interactions between plants and their environments, including the well-known functions of attracting pollinators and protecting plants from herbivores/fungi attacks. However, to date, there have not been reports of holistic volatile profiling of the various tissues of a single plant species, even for the model plant species. In this study, we qualitatively and quantitatively analyzed 85 volatile chemicals, including 36 volatile terpenes, in 23 different tissues of cucumber (Cucumis sativus) plants using solid-phase microextraction combined with gas chromatography-mass spectrometry. Most volatile chemicals were found to occur in a highly tissue-specific manner. The consensus transcriptomes for each of the 23 cucumber tissues were generated with RNA sequencing data and used in volatile organic compound-gene correlation analysis to screen for candidate genes likely to be involved in cucumber volatile biosynthetic pathways. In vitro biochemical characterization of the candidate enzymes demonstrated that TERPENE SYNTHASE11 (TPS11)/TPS14, TPS01, and TPS15 were responsible for volatile terpenoid production in the roots, flowers, and fruit tissues of cucumber plants, respectively. A functional heteromeric geranyl(geranyl) pyrophosphate synthase, composed of an inactive small subunit (type I) and an active large subunit, was demonstrated to play a key role in monoterpene production in cucumber. In addition to establishing a standard workflow for the elucidation of plant volatile biosynthetic pathways, the knowledge generated from this study lays a solid foundation for future investigations of both the physiological functions of cucumber volatiles and aspects of cucumber flavor improvement.

The coronavirus disease 2019 (COVID-2019) that emerged in Wuhan, China, has rapidly spread to many countries across all six WHO regions. However, its pathobiology remains incompletely understood and many efforts are underway to study it worldwide. To clarify its pathogenesis to some extent, it will inevitably require lots of COVID-2019-associated pathological autopsies. Pathologists from all over the world have raised concerns with pathological autopsy relating to COVID-2019. The issue of whether a person died from COVID-2019 infection or not is always an ambiguous problem in some cases, and ongoing epidemiology from China may shed light on it. This review retrospectively summarizes the research status of pathological autopsy for COVID-2019 deaths in China, which will be important for the cause of death, prevention, control and clinical strategies of COVID-2019. Moreover, it points out several challenges at autopsy. We believe pathological studies from China enable to correlate clinical symptoms and pathological features of COVID-2019 for doctors and provide an insight into COVID-2019 disease.

Purpose The implementation of radiomics and machine learning (ML) techniques on analyzing two‐dimensional gamma maps has been demonstrated superior to the conventional gamma analysis for error identification in intensity modulated radiotherapy (IMRT) quality assurance (QA). Recently, the Structural SIMilarity (SSIM) sub‐index maps were shown to be able to reveal the error types of the dose distributions. In this study, we aimed to apply radiomics analysis on SSIM sub‐index maps and develop ML models to classify delivery errors in patient‐specific dynamic IMRT QA. Methods Twenty‐one sliding‐window IMRT plans of 180 beams for three treatment sites were involved in this study. Four types of machine‐related errors of various magnitudes were simulated for each beam at each control point, including the monitor unit (MU) variations, same‐directional and opposite‐directional shifts of the multileaf collimators (MLCs) and random mispositioning of the MLCs. In the QA process, a total of 1620 portal dose (PD) images were acquired for the beams with and without errors. The predicted PD images of the original beams were set as references. To quantify the agreement between a measured PD image and the corresponding predicted PD image, four difference maps including three SSIM sub‐index maps, and one dose difference‐derived map were calculated. Then, radiomic features were extracted from the four difference maps of each measured PD image. We tested four typical classifiers including linear discriminant classifier (LDC), two supporting vector machine (SVM) classifiers, and random forest (RF) for this multiclass classification task. A nested cross‐validation scheme was used for model evaluations, where the SVM recursive feature elimination method was applied for feature selection. Finally, the performance of the ML model on identifying the error‐free and the erroneous cases was compared to that of the conventional gamma analysis. Results The statistics of the selected features showed that all of the difference maps and the feature categories made balanced contributions to solve this classification task. Best performance was achieved by the Linear‐SVM model with average overall classification accuracy of 0.86. Specifically, the average classification accuracies of the shift, opening, and the random errors were around 0.9. Moreover, ~80% of error‐free and MU errors were correctly classified. Using gamma analysis, the 3 mm/3% criterion was found insensitive to errors (sensitivity was only 0.33). Although the sensitivity to errors with the 2 mm/2% criterion increased to 0.79, still 8% worse than that of the ML model. Conclusions We proposed an ML‐based method for machine‐related error identification in patient‐specific dynamic IMRT QA, where radiomic analysis on SSIM sub‐index maps were used for feature extraction. With extensive validation to select the best features and classifiers, high accuracies in error classification were achieved. Compared with the conventional gamma threshold method, this approach has great potential in error...

Background: Copy number variation (CNV), a complex genomic rearrangement, has been extensively studied in humans and other organisms. In plants, CNVs of several genes were found to be responsible for various important traits; however, the cause and consequence of CNVs remains largely unknown. Recently released next-generation sequencing (NGS) data provide an opportunity for a genome-wide study of CNVs in rice. Results: Here, by an NGS-based approach, we generated a CNV map comprising 9,196 deletions compared to the reference genome 'Nipponbare'. Using Oryza glaberrima as the outgroup, 80 % of the CNV events turned out to be insertions in Nipponbare. There were 2,806 annotated genes affected by these CNV events. We experimentally validated 28 functional CNV genes including OsMADS56, BPH14, OsDCL2b and OsMADS30, implying that CNVs might have contributed to phenotypic variations in rice. Most CNV genes were found to be located in non-co-linear positions by comparison to O. glaberrima. One of the origins of these non-co-linear genes was genomic duplications caused by transposon activity or double-strand break repair. Comprehensive analysis of mutation mechanisms suggested an abundance of CNVs formed by non-homologous end-joining and mobile element insertion. Conclusions: This study showed the impact and origin of copy number variations in rice on a genomic scale.

PurposeTo test if a RapidPlan DVH estimation model and its training plans can be improved interactively through a closed‐loop evolution process.Methods and materialsEighty‐one manual plans (P0) that were used to configure an initial rectal RapidPlan model (M0) were reoptimized using M0 (closed‐loop), yielding 81 P1 plans. The 75 improved P1 (P1+) and the remaining 6 P0 were used to configure model M1. The 81 training plans were reoptimized again using M1, producing 23 P2 plans that were superior to both their P0 and P1 forms (P2+). Hence, the knowledge base of model M2 composed of 6 P0, 52 P1+, and 23 P2+. Models were tested dosimetrically on 30 VMAT validation cases (Pv) that were not used for training, yielding Pv(M0), Pv(M1), and Pv(M2) respectively. The 30 Pv were also optimized by M2_new as trained by the library of M2 and 30 Pv(M0).ResultsBased on comparable target dose coverage, the first closed‐loop reoptimization significantly (P < 0.01) reduced the 81 training plans’ mean dose to femoral head, urinary bladder, and small bowel by 2.65 Gy/15.63%, 2.06 Gy/8.11%, and 1.47 Gy/6.31% respectively, which were further reduced significantly (P < 0.01) in the second closed‐loop reoptimization by 0.04 Gy/0.28%, 0.18 Gy/0.77%, 0.22 Gy/1.01% respectively. However, open‐loop VMAT validations displayed more complex and intertwined plan quality changes: mean dose to urinary bladder and small bowel decreased monotonically using M1 (by 0.34 Gy/1.47%, 0.25 Gy/1.13%) and M2 (by 0.36 Gy/1.56%, 0.30 Gy/1.36%) than using M0. However, mean dose to femoral head increased by 0.81 Gy/6.64% (M1) and 0.91 Gy/7.46% (M2) than using M0. The overfitting problem was relieved by applying model M2_new.ConclusionsThe RapidPlan model and its constituent plans can improve each other interactively through a closed‐loop evolution process. Incorporating new patients into the original training library can improve the RapidPlan model and the upcoming plans interactively.

Subversion of the host cell cycle to facilitate viral replication is a common feature of coronavirus infections. Coronavirus nucleocapsid (N) protein could modulate host cell cycle, but the mechanistic details remain largely unknown. Here, we investigated manipulation of porcine epidemic diarrhea virus (PEDV) N protein on cell cycle and its influence on viral replication. Results indicated that PEDV N-induced Vero E6 cell cycle arrest at S-phase, which promoted viral replication ( P < 0.05). S-phase arrest was dependent on N protein nuclear localization signal S 71 NWHFYYLGTGPHADLRYRT 90 and interaction between N protein and p53. In the nucleus, the binding of N protein to p53 maintained consistently high-level expression of p53, which activated p53-DREAM pathway. The key domain of the N protein interacting with p53 was revealed to be S 171 RGNSQNRGNNQGRGASQNRGGNN 194 (N S171-N194 ), in which G 183 RG 185 are core sites. N S171-N194 and G 183 RG 185 were essential for N-induced S-phase arrest. Moreover, small molecular drugs targeting the N S171-N194 domain of PEDV N protein were screened through molecular docking. Hyperoside could antagonize N protein-induced S-phase arrest by interfering with interaction between N protein and p53 and inhibit viral replication ( P < 0.05). The above experiments were also validated in porcine intestinal cells, and resulting data were in line with that of Vero E6 cells. Therefore, these results revealed that PEDV N protein interacted with p53 to activate p53-DREAM pathway, and subsequently induced S-phase arrest to create a favorable environment for virus replication. These findings provided new insight into the PEDV-host interaction and the design of novel antiviral strategies against PEDV.

Prohibitin (PHB) is a highly conserved major sperm mitochondrial membrane protein whose absence in somatic cells is associated with mitochondrial membrane depolarization and increased generation of reactive oxygen species (ROS). Our recent findings suggest that high levels of oxidants in human semen may contribute to male infertility and that sperm motility could be the earliest and most sensitive indicator of oxidative damage. Based on PHB's roles in mitochondrial sub-compartmentalization and respiratory chain assembly, we examine sperm PHB expression and mitochondrial membrane potential (MITO) in infertile men with poor sperm motility (asthenospermia, A) and/or low sperm concentrations (oligoasthenospermia, OA). Here, we demonstrate that MITO is significantly lower in sperm from A and OA subjects than in normospermic (N) subjects; the decrease is more severe for OA than for A subjects. PHB expression is also significantly lower in sperm from A and OA subjects. Significantly positive correlations are found among PHB expression, MITO, and sperm motility in normospermic, asthenospermic, and oligoasthenospermic subjects. Collectively, our observations lead to the hypothesis that PHB expression is an indicator of sperm quality in infertile men, and that it regulates sperm motility via an alteration in MITO and increased ROS levels.